Comparison of Machine Learning Algorithms for Flood Susceptibility Mapping

Remote Sensing, Journal Year: 2022, Volume and Issue: 15(1), P. 192 - 192

Published: Dec. 29, 2022

Floods are one of the most destructive natural disasters, causing financial and human losses every year. As a result, reliable Flood Susceptibility Mapping (FSM) is required for effective flood management reducing its harmful effects. In this study, new machine learning model based on Cascade Forest Model (CFM) was developed FSM. Satellite imagery, historical reports, field data were used to determine flood-inundated areas. The database included 21 flood-conditioning factors obtained from different sources. performance proposed CFM evaluated over two study areas, results compared with those other six methods, including Support Vector Machine (SVM), Decision Tree (DT), Random (RF), Deep Neural Network (DNN), Light Gradient Boosting (LightGBM), Extreme (XGBoost), Categorical (CatBoost). result showed produced highest accuracy models both Overall Accuracy (AC), Kappa Coefficient (KC), Area Under Receiver Operating Characteristic Curve (AUC) more than 95%, 0.8, 0.95, respectively. Most these recognized southwestern part Karun basin, northern northwestern regions Gorganrud basin as susceptible

Language: Английский

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Spatial Downscaling of GRACE Data Based on XGBoost Model for Improved Understanding of Hydrological Droughts in the Indus Basin Irrigation System (IBIS)

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(4), P. 873 - 873

Published: Feb. 4, 2023

Climate change may cause severe hydrological droughts, leading to water shortages which will require be assessed using high-resolution data. Gravity Recovery and Experiment (GRACE) satellite Terrestrial Water Storage (TWSA) estimates offer a promising solution monitor drought, but its coarse resolution (1°) limits applications small regions of the Indus Basin Irrigation System (IBIS). Here we employed machine learning models such as Extreme Gradient Boosting (XGBoost) Artificial Neural Network (ANN) downscale GRACE TWSA from 1° 0.25°. The findings revealed that XGBoost model outperformed ANN with Nash Sutcliff Efficiency (NSE) (0.99), Pearson correlation (R) Root Mean Square Error (RMSE) (5.22 mm), Absolute (MAE) (2.75 mm) between predicted GRACE-derived TWSA. Further, Deficit Index (WSDI) WSD (Water Deficit) were used determine severity episodes respectively. results WSDI exhibited strong agreement when compared Standardized Precipitation Evapotranspiration (SPEI) at different time scales (1-, 3-, 6-months) self-calibrated Palmer Drought Severity (sc-PDSI). Moreover, IBIS had experienced increasing drought episodes, e.g., eight detected within years 2010 2016 −1.20 −1.28 total −496.99 mm −734.01 mm, Partial Least Regression (PLSR) climatic variables indicated potential evaporation largest influence on after precipitation. this study helpful for drought-related decision-making in IBIS.

Language: Английский

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Annual runoff coefficient variation in a changing environment: a global perspective

Environmental Research Letters, Journal Year: 2022, Volume and Issue: 17(6), P. 064006 - 064006

Published: May 13, 2022

Abstract Assessing variations in the annual runoff coefficient (RC) on a basin scale is crucial for understanding hydrological cycle under natural and anthropogenic changes, yet systematic global assessment remains unexamined from water-balance perspective. Here, we combine observation-based precipitation datasets to quantify basin-averaged RC changes 433 major river basins during period 1985–2014. Thereafter, ratios of terrestrial water storage evaporation (SC EC, respectively) are obtained evaluate factors driving changes. The results show that 12.93% experience significant decreasing trends RC, with slopes ranging −0.55 ± 0.17% yr −1 −0.05 0.02% , while 6.47% increasing RCs 0.09 0.04% 0.56 . A higher percentage (62.95%) reveal regions considerable human intervention compared those (58.24%) dominant variability. Changes EC dominate over 79.68% both trends, maximum contribution (53.65%) transpiration, among other partitioned components. Corroborated inferences explicit investigation Yangtze River highlight robustness our managers policymakers.

Language: Английский

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Exploring the contribution of environmental factors to evapotranspiration dynamics in the Three-River-Source region, China

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 626, P. 130222 - 130222

Published: Sept. 25, 2023

Language: Английский

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Mapping of Flood-Prone Areas Utilizing GIS Techniques and Remote Sensing: A Case Study of Duhok, Kurdistan Region of Iraq

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(4), P. 1102 - 1102

Published: Feb. 17, 2023

One of the most common types natural disaster, floods can happen anywhere on Earth, except in polar regions. The severity damage caused by flooding be reduced putting proper management and protocols into place. Using remote sensing a geospatial methodology, this study attempts to identify flood-vulnerable areas central district Duhok, Iraq. analytical hierarchy process (AHP) technique was used give relative weights 12 contributing parameters, including elevation, slope, distance from river, rainfall, land use cover, soil, lithology, topographic roughness index, wetness aspect, sediment transport stream power index order calculate Flood Hazard Index (FHI). importance each criterion revealed sensitivity analysis parameter values. This research developed final flood susceptibility map identified high-susceptible zones. classified very low high classifications for its potential hazard. generated indicates that 44.72 km2 total area Duhok city has flooding, these require significant attention government authorities reduce vulnerability.

Language: Английский

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State-of-the-art review: Operation of multi-purpose reservoirs during flood season

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 618, P. 129165 - 129165

Published: Jan. 25, 2023

Language: Английский

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Projection of drought-flood abrupt alternation in a humid subtropical region under changing climate

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 624, P. 129875 - 129875

Published: June 28, 2023

Language: Английский

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Enhancing flow rate prediction of the Chao Phraya River Basin using SWAT–LSTM model coupling

Journal of Hydrology Regional Studies, Journal Year: 2024, Volume and Issue: 53, P. 101820 - 101820

Published: May 24, 2024

Chao Phraya River Basin—a major river with unique characteristics located in Thailand. This study sought to simulate the flow rates Basin, which is a tidal that poses challenges traditional modeling approaches. The soil and water assessment tool (SWAT) hydrological model extensively employed for simulating rates. However, limitations arise applying SWAT Basin due its nature, resulting an unsatisfactory performance. To address this, long short-term memory (LSTM) model, i.e., SWAT–LSTM was introduced complement model. collaborative coupling of information derived from LSTM notably enhanced improvement assessed using Nash-Sutcliffe efficiency (NSE), demonstrating increase 0.13 0.72. incorporation topographic static data also investigated provide basic basin results yielded NSE exceeding 0.79. shoreline level identified as crucial input feature indicating patterns. findings highlight effectiveness predicting rates, implying their applicability similar scenarios across different basins.

Language: Английский

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Global evaluation of the “dry gets drier, and wet gets wetter” paradigm from a terrestrial water storage change perspective

Hydrology and earth system sciences, Journal Year: 2022, Volume and Issue: 26(24), P. 6457 - 6476

Published: Dec. 22, 2022

Abstract. The “dry gets drier, and wet wetter” (DDWW) paradigm has been widely used to summarize the expected trends of global hydrologic cycle under climate change. However, is largely conditioned by choice different metrics datasets still comprehensively unexplored from perspective terrestrial water storage anomalies (TWSAs). Considering essential role TWSAs in wetting drying land system, here we built upon a large ensemble TWSA datasets, including satellite-based products, hydrological models, surface models evaluate DDWW hypothesis during historical (1985–2014) future (2071–2100) periods various scenarios with 0.05 significance level (for trend estimates). We find that 11.01 %–40.84 % (range datasets) confirms paradigm, while 10.21 %–35.43 area shows opposite pattern period. In future, challenged, percentage supporting lower than 18 both DDWW-validated DDWW-opposed proportion increasing along intensification emission scenarios. show choices data sources can reasonably influence test results up 4-fold difference. Our findings will provide insights implications for

Language: Английский

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Leveraging machine learning methods to quantify 50 years of dwindling groundwater in India

The Science of The Total Environment, Journal Year: 2022, Volume and Issue: 835, P. 155474 - 155474

Published: April 27, 2022

Language: Английский

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